Dental medical record device and dental medical record method thereof

ABSTRACT

A dental medical record device and a dental medical record method, in which: an image, such as a panoramic photo, a scan image, and a camera image of a patient&#39;s oral cavity, is received via artificial intelligence, and charting is performed using the artificial intelligence; and medical records for a treatment area can be read in association with a chart by clicking the treatment area in the image.

TECHNICAL FIELD

The present invention provides a dental medical record device forcreating a dental medical record and a dental medical record methodthereof.

BACKGROUND ART

During dental treatment, to create a dental medical record, a doctorlooks at the panoramic photo captured around a patient's teeth andperforms an overall basic charting. Further, the doctor adds the dataobtained during the oral examination by the patient to the dentalmedical record. After evaluating the patient's teeth, the doctor marksthe dental medical record with text or symbols.

In this case, the doctor must evaluate each of the multiple teeth. Dueto possible infection issues between the time of examination and thetime of recording, instead of looking at the patient and filling out thedental medical record, the doctor dictates to an assistant doctor toallow the assistant doctor to record, or an assistant dictates and thedoctor transcribes the dental medical record. Further, in order toreview the treatment history for each of multiple teeth, the doctorshould separately search for the medical record and receipt history foreach tooth.

Currently, it thus takes a long time to create a dental medical recordduring dental treatment, and errors may occur while the assistant doctortranscribes when writing a dental medical record. Further, there is aproblem in that it is difficult to dictate and record immediately whencharting in a hospital or private clinic without a specialist.

DETAILED DESCRIPTION OF THE INVENTION Technical Problem

Embodiments provide a dental medical record device and a dental medicalrecord method capable of reducing the time and costs required forcreating a dental medical record.

Embodiments also provide a dental medical record device and a dentalmedical record method capable of enhancing the accuracy of prognosis andreducing the possibility of cross-infection of patients.

Embodiments also provide a dental medical record device and a dentalmedical record method capable of easily creating, storing, and viewingdental medical records.

However, the objects of the embodiments are not limited thereto, andother objects may also be present.

Technical Solution

The disclosure provides a dental medical record device and a dentalmedical record method that receive an image, such as a panoramic photo,a scan image, and a camera image, for a patient's oral cavity throughartificial intelligence, perform charting using artificial intelligence,and if the treatment site is clicked in the corresponding image, allowsthe medical records for the treatment site in association with thechart.

In an aspect, a dental medical record method according to an embodimentcomprises reading an image for a patient's oral cavity to mark thepatient's oral cavity condition on a prognosis chart, using a textformat, symbols, or both text and symbols and displaying the image andthe prognosis chart.

In another aspect, a dental medical record device according to anotherembodiment comprises an input unit receiving an image for a patient'soral cavity, a controller reading the image for the patient's oralcavity to determine the patient's oral cavity condition and marking thepatient's oral cavity condition on a prognosis chart, using a textformat, symbols, or both text and symbols and an output unit displayingthe image and the prognosis chart.

In another aspect, a non-transitory computer-readable storage mediumaccording to another embodiment stores instructions, when executed byone or more processors, instructions that, when executed by one or moreprocessors, enable the one or more processors to perform a dentalmedical record method.

When executed by the one or more processors, the instructions enable thecomputer device to read an image for a patient's oral cavity to mark thepatient's oral cavity condition on a prognosis chart, using a textformat, symbols, or both text and symbols and display the image and theprognosis chart.

Advantageous Effects

By the dental medical record device and the dental medical record methodaccording to embodiments, it is possible to reduce the time and costsrequired for creating a dental medical record.

By the dental medical record device and the dental medical record methodaccording to embodiments, it is also possible to enhance the accuracy ofprognosis and reduce the possibility of cross-infection of patients.

By the dental medical record device and the dental medical record methodaccording to embodiments, a dental medical record device and a dentalmedical record method capable of easily creating, storing, and viewingdental medical records are provided.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a dental medical record method according to anembodiment;

FIG. 2 is an example of an image captured for a patient's oral cavity;

FIG. 3 is an example of an EMR chart;

FIG. 4 is an example illustrating a text and symbol chart;

FIG. 5 is an example of a photo illustrating a patient's teeth anddental formula;

FIG. 6 is an example of a photo illustrating a patient's periodontalcondition;

FIGS. 7A and 7B are examples of photos illustrating a patient's dentalcaries and restoration state; FIGS. 7C and 7D are training images of aguide chip and a guide sample;

FIGS. 8A and 8B are an example of viewing information about a treatmentsite in a pop-up;

FIGS. 9 and 10 are schematic views illustrating the operation of adental medical record device;

FIG. 11 is a view of a configuration of a deep learning model used in adental medical record method 100 according to an embodiment of FIG. 1 ;and

FIG. 12 is a block diagram of a dental medical record device accordingto another embodiment.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, exemplary embodiments of the inventive concept will bedescribed in detail with reference to the accompanying drawings. Theinventive concept, however, may be modified in various different ways,and should not be construed as limited to the embodiments set forthherein. Like reference denotations may be used to refer to the same orsimilar elements throughout the specification and the drawings. However,the present invention may be implemented in other various forms and isnot limited to the embodiments set forth herein. For clarity of thedisclosure, irrelevant parts are removed from the drawings, and similarreference denotations are used to refer to similar elements throughoutthe specification.

In embodiments of the present invention, when an element is “connected”with another element, the element may be “directly connected” with theother element, or the element may be “electrically connected” with theother element via an intervening element. When an element “comprises” or“includes” another element, the element may further include, but ratherthan excluding, the other element, and the terms “comprise” and“include” should be appreciated as not excluding the possibility ofpresence or adding one or more features, numbers, steps, operations,elements, parts, or combinations thereof.

When the measurement of an element is modified by the term “about” or“substantially,” if a production or material tolerance is provided forthe element, the term “about” or “substantially” is used to indicatethat the element has the same or a close value to the measurement and isused for a better understanding of the present invention or forpreventing any unscrupulous infringement of the disclosure where theexact or absolute numbers are mentioned. As used herein, “step of” A or“step A-ing” does not necessarily mean that the step is one for A.

As used herein, the term “part” may mean a unit or device implemented inhardware, software, or a combination thereof. One unit may beimplemented with two or more hardware devices or components, or two ormore units may be implemented in a single hardware device or component.

As used herein, some of the operations or functions described to beperformed by a terminal or device may be, instead of the terminal ordevice, performed by a server connected with the terminal or device.Likewise, some of the operations or functions described to be performedby a server may be performed by a terminal or device connected with theserver, instead of the server.

As used herein, some of the operations or functions described to bemapped or matched with a terminal may be interpreted as mapping ormatching the unique number of the terminal, which is identificationinformation about the terminal, or personal identification information.

Hereinafter, embodiments of the disclosure are described in detail withreference to the accompanying drawings.

FIG. 1 is a flowchart of a dental medical record method according to anembodiment.

Referring to FIG. 1 , a dental medical record method 100 according to anembodiment includes the step S110 of reading an image for a patient'soral cavity and marking a prognosis chart with text or a symbol or in acombination of text and a symbol for the patient's oral cavity conditionand the step S120 of displaying the image and the prognosis chart.

The image may be one of a panoramic photo, a scan image, or a cameraimage obtained by one or two or more of radiography, oral camera,scanning device, and occlusion check data for the patient's oral cavity,as is described below with reference to FIG. 2 .

Further, the patient's oral cavity condition may be at least one of thedental formula, dental conditions, or surrounding structures of theteeth.

In the step S110 of marking the prognosis chart, artificial intelligenceis continuously trained with continuously collected training images andaccumulated chart data so that artificial intelligence reads the images,as described below with reference to FIG. 11 . As in the above-describeddrawings, e.g., as shown in FIGS. 7C and 7D described below, artificialintelligence may be trained with training images including, e.g., aguide chip or a guide sample. As such, since the image including theguide chip or guide sample is obtained in the process of pre-treatingthe training image or obtaining the image, it is possible to enhance thetraining effect and the accuracy of determination in the prognosis chartmarking step S110.

Artificial intelligence may automatically learn the features for inputvalues by being trained with a large amount of data from a deep neuralnetwork composed of a multi-layered network, and train the multi-layerednetwork to minimize errors in prediction accuracy and read images.

As described below with reference to FIGS. 8 to 10 , in the step S120 ofdisplaying the image and chart, if a specific treatment site of theimage is selected, information about the treatment site is displayed,and if the image of the treatment site is selected, a treatment summarymemo window is displayed, and if the treatment summary memo window isselected, a chart for the treatment site is output.

FIG. 2 is an example of an image captured by the patient's oral cavity.FIG. 2 is a panoramic photo captured for the patient's oral cavity,illustrating the patient's oral cavity condition.

Referring to FIGS. 1 and 2 , the dental medical record method 100according to an embodiment may, in the step S110 of marking theprognosis chart, read information about the dental formula, teethconditions (e.g., periodontal conditions (gum conditions), dentalcaries, tooth loss, restoration, prosthetics, implants, wisdom teeth),and surrounding structures of the teeth (e.g., temporomandibular joint)based on the image, such as the panoramic photo of FIG. 2 .

According to an embodiment, the dental medical record method 100 mayread images, such as panoramic photos, scan images or camera images forthe oral cavity, and move the results to an electronic medical record(EMR) chart, thereby obtaining a prognosis effect.

FIG. 3 is an example of an EMR chart. The EMR chart of FIG. 3 is asymbol chart composed of teeth-related symbols.

Referring to FIGS. 1 and 3 , the dental medical record method 100according to an embodiment may, in the step S110 of marking theprognosis chart, read the images, such as panoramic photos, scan imagesor camera images for the oral cavity, determining whether the patient'soral cavity condition is normal or whether there is a disease in theteeth, alveolar bone, and jaw bone. Such images may be obtained by meansof radiography, oral camera, scanning device, occlusion check data, andthe like.

Further, the dental medical record device may display the content readthrough the above-described images, in the format of text, symbols, orin a combination of text and symbols.

FIG. 4 is an example illustrating a text and symbol chart.

Referring to FIG. 4 , the text chart 140 is a prognosis chart in whichthe content read through the images is depicted in narrative text in thestep S110 of marking the prognosis chart.

For example, the text chart 140 may represent information indicatingthat tooth #16 has secondary caries, tooth #44 has a periapical lesion,teeth #41, 42, and 43 have attrition, and teeth #47, 46, and 37 have OldAM.

The symbol chart 142 is a chart in which the content read through theimages is depicted through various symbols. For example, the symbolchart may depict tooth loss, periodontal disease, dental caries,restoration, and the like, based on standard symbol standards.

The dental medical record method 100 according to an embodiment may readthe images through artificial intelligence using a machine learningmodel, a deep learning model, etc., as is described below with referenceto FIG. 10 . The artificial intelligence used in the dental medicalrecord device may learn the diseases that occur in teeth, alveolarbones, and jaw bones.

In this case, the artificial intelligence may set a dividing criterionwhile inputting video data for normal structures and lesions in the oralcavity during the learning process.

As an example, when the alveolar bone is positioned up to 1 mm below thearea where the crown and the root meet, the alveolar bone may berecognized as normal. As another example, if there is no tooth in thealveolar bone, it may be recognized as tooth loss.

Meanwhile, the diagnosis of lesions using artificial intelligence may beperformed based on factors, such as tooth color, tooth loss, andradiography information in the images (e.g., houndsfield scale).

The dental medical record method 100 according to an embodiment maycontinuously train the artificial intelligence with continuouslycollected training images and accumulated chart data, thereby increasingthe accuracy for the artificial intelligence to read the images.

Further, the dental medical record method 100 according to an embodimentmay, after reading the images through the artificial intelligence, chartthe same and associate the images with the patient's treatment andreceipt content, in the step S110 of marking the prognosis chart. Forexample, if the doctor clicks on the image, a pop-up 144 indicatinginformation about the treatment date for the clicked area and thereceipt memo window may be generated. Further, if the doctor clicks thereceipt memo window on the pop-up 144, information about the chartassociated with the corresponding memo window may be accessed.

FIG. 5 is an example of a photo illustrating a patient's teeth anddental formula.

Referring to FIG. 5 , the dental formula is a scheme for distinguishingthe teeth and divides the left, right, top, and bottom (1 to 8) of thecenter of the patient's oral cavity and distinguishes incisors andmolars with respect to canine teeth.

The roots of the teeth are positioned inside the bone (#11 to #48), thecrown is positioned outside the bone. The teeth are nearly perpendicularto the bone.

FIG. 6 is an example of a photo illustrating a patient's periodontalcondition.

The dental medical record method 100 according to an embodiment maydetermine that it is normal if the alveolar bone is positioned up to 1mm in the root direction from the site (arrow) where the crown and theroot of the tooth meet and that it is an alveolar bone loss if thealveolar bone is positioned therebelow. Alveolar bone loss includeshorizontal loss and vertical loss. Further, the dental medical recordmethod 100 according to an embodiment may identify the state of thefurcation, which is the site where the root of the molar is split.

The dental medical record method 100 according to an embodimentdetermines the patient's periodontal condition of alveolar bone lossafter reading the image shown in FIG. 6 through artificial intelligencein the step S110 of marking the prognosis chart.

FIGS. 7A and 7B are examples of photos illustrating a patient's dentalcaries and restoration state.

As shown in FIG. 7A, dental caries means a defect in enamel, dentin, orpulp included in the tooth.

The dental medical record method 100 according to an embodimentdetermines the patient's periodontal condition of dental caries afterreading the image shown in FIG. 7A through artificial intelligence inthe step S110 of marking the prognosis chart.

As shown in FIG. 7B, among the dental restorations, crown A does nottransmit the radiation throughout the entire crown and, among the dentalrestorations, inlay or amalgam B does not transmit the radiation for aportion of the crown. Among the dental restorations, post C is shown bya dark white straight line within the root of the tooth. The tooth Dthat has undergone root canal treatment is shown by a white curve alongthe root line at the center of the root of the tooth.

The dental medical record method 100 according to an embodiment readsthe image shown in FIG. 7B through artificial intelligence, marks crownin position A, inlay or amalgam in position B, post in position C, androot canal in position D in the step S110 of marking the prognosischart.

FIG. 7C is an example of a panoramic image including a guide chip,captured by a panoramic image capture device.

The panoramic image capture device positions a capture subject, e.g., apatient, on a cylindrical body equipped with an X-ray source, and thenrotates the X-ray source and panoramic image detector around thepatient, thereby obtaining a plurality of images. The panoramic imagecapture device naturally connects these images to obtain a panoramicimage as shown in FIG. 7C. When capturing the panoramic image, guidechips having different gray scales (e.g., houndsfield unit (HU)) aremounted on the panorama detector to obtain a panoramic image including aguide chip as shown in FIG. 7C.

The artificial intelligence may be trained with the training imagesincluding guide chips.

Since the artificial intelligence is trained with the guidesample-containing training images, the patient's periodontal condition,such as dental caries, is more accurately determined after reading theimages through the artificial intelligence in the step S110 of markingthe prognosis chart.

In the step S110 of marking the prognosis chart, when the image is readthrough the trained artificial intelligence, the difference in dentalcaries and the case in which the transmission of radiations has beenreduced, such as enamel, dentin, or pulp, may be more accurately read ascompared with the normal tooth tissue in the panoramic image. Further,even when the radiodensity differs in the panoramic image depending on,e.g., the anatomical structure of each patient, the size of the patient,and the posture, it is possible to more accurately read the periodontalcondition of dental caries.

In the above-described example, in the step S110 of marking theprognosis chart, the dental caries is determined after reading the imageincluding the guide chip shown in FIG. 7C through artificialintelligence. For the same reason, after reading the image including theguide chip shown in FIG. 7C through artificial intelligence, it ispossible to accurately determine periodontal conditions or diagnoseother lesions, such as periodontitis, apical lesions, and alveolar boneloss.

FIG. 7D is an example of an image including a guide sample, captured bya camera or a scanner.

When capturing with a camera or scanner, an image including a guidesample is obtained as shown in FIG. 7D. The guide sample includes acolor number and a color corresponding to the color number.

The artificial intelligence may be trained with the training imagesincluding guide samples. In the image, the tooth color is affected bylighting and shadow, the capture camera. Accordingly, since theartificial intelligence is trained with the guide sample-containingtraining images, the patient's tooth color may be more accuratelydetermined after reading the images through the artificial intelligencein the step S110 of marking the prognosis chart.

Therefore, in the step S120 of displaying the image and the prognosischart, if a specific tooth is clicked on the image, the color of thespecific tooth, e.g., an optical tooth color, presented by the trainedartificial intelligence is indicated with the color number (e.g., N3)and/or color (e.g., yellow) of the guide sample, on the image. Thus, itis possible to suggest the color of the restoration or prosthesis thatbest matches the color of the patient's teeth, providing an effective,aesthetic treatment method.

FIGS. 8A and 8B are examples of viewing information about a treatmentsite in a pop-up.

Referring to FIG. 8A, in the step S120 of displaying the image and thechart, if the doctor clicks on a specific treatment site on the image,information about the treatment site (e.g., treatment title, date, orreceipt memo window) is displayed. The image of the treatment site andinformation about the treatment site may be linked in the form of ahyperlink. For example, as the information about the treatment site,“Jan. 2, 2020 #44, 45 extractions, 44 immediate implants, receipt:200,000 won” is displayed as shown in FIG. 8A.

Referring to FIG. 8B, if the image of the corresponding treatment siteis clicked, a treatment summary memo (e.g., treatment title, date, andreceipt) window is displayed, and if the treatment summary memo windowis clicked, a chart for the treatment site may be output.

Further, if the receiving memo window is clicked, a chart linked to thereceiving memo window may be output.

FIGS. 9 and 10 are schematic views illustrating the operation of adental medical record method according to an embodiment.

Referring to FIGS. 1 and 9 and 10 , a dental medical record method 100according to an embodiment may read an image (e.g., a panoramic photo)of the patient's oral cavity and then create a prognosis chart composedof text or symbols in the step S110 of marking the prognosis chart.

The dental medical record method 100 according to an embodiment mayenhance the accuracy of creating the prognosis chart by continuouslylearning individually input information or accumulated chart recordsthrough artificial intelligence using a machine learning model or a deeplearning model in the step S110 of marking the prognosis chart.

In the step S120 of displaying the image and the chart, the dentalmedical record method 100 according to an embodiment may generate atreatment popup 144 including information, such as treatment title,date, and receipt memo window, if the doctor clicks the treatment siteon the image (e.g., panoramic photo) for the patient's oral cavity andoutput a chart linked to the receipt memo window if the doctor clicksthe receipt memo window.

By the dental medical record method 100 according to an embodiment, evena hospital or private clinic without a specialist may enhanceconvenience and accuracy when recording dental medical records. Further,it allows the specialist to focus on studying specialized care ratherthan dictation. Further, it is possible to increase the accuracy ofdetermining the patient's oral condition based on the accumulated imageinformation and charting data.

FIG. 11 is a view of a configuration of a deep learning model used in adental medical record method 100 according to an embodiment of FIG. 1 .

Referring to FIG. 11 , a deep learning model 221 that may be used in thedental medical record method 100 according to an embodiment may be amodel in which the artificial neural networks is composed of multiplelayers stacked. In other words, the deep learning model is a model insuch a form as to train the network to minimize errors in predictionaccuracy, by automatically learning features for input values bylearning massive data in the deep neural network composed of amulti-layered network.

The above-described deep learning model 221 may be a convolutionalneural network (CNN), deep hierarchical network (DHN), convolutionaldeep belief network (CDBN), deconvolutional deep network (DDN),recurrent neural network (RNN), or generative adversarial network (GAN),but the present invention is not limited thereto, and may use variousdeep learning models that may be used currently or in the future.

The above-described deep learning model 221 may be implemented through adeep learning framework. The deep learning framework plays a role toprovide a library of functions commonly used when developing the deeplearning model 221 and to support the system software or hardwareplatform to be properly used. In this embodiment, the deep learningmodel 221 may be implemented using any deep learning framework that hasbeen currently disclosed or will be disclosed in the future.

Referring back to FIG. 11 , the deep learning model 221 includes afeature extraction part 222 that extracts features for the image byperforming convolution and subsampling on the input image and an outputpart 224 that marks the prognosis chart with the patient's oral cavitycondition in the format of text, symbols, or both text and symbols,using the extracted features.

Convolution creates a feature map using a plurality of filters for eacharea of the medical image in the convolution layer. Subsampling orpooling reduces the size of the feature map in the subsampling layer toextract features for the image that is invariant to a change in positionor rotation.

The feature extraction part 222 may repeat convolution and/orsubsampling to extract various levels of features from low-levelfeatures, such as dots, lines, or planes, to complex, meaningfulhigh-level features, from the medical image.

The deep learning model, e.g., a CNN-based deep learning model, targetsoptimally training the parameters present in each individual layer inthe feature extraction part 222 and the output part 224. In the deeplearning model, the order of data determines the initial parametervalue.

The deep learning model 221 may apply random sampling (random in dataorder) and a regulation technique. Random sampling means that the orderof the training data learned from the training data set is different.

The regulation technique is a technique that reduces overfitting, inwhich a deep learning model over-trained with training data includingeven noise deteriorates test or diagnosis accuracy. The regulationtechnique may be, e.g., a drop-out technique or a drop-connectedtechnique.

The drop-out technique is a method for performing learning with theprobable parameter value designated as 0 for a specific node. Thedrop-connected technique is a method for performing learning with theconnections between nodes dropped. Although the drop-out technique, asthe regulation technique, is described below as an example, it may beany current or future technique or algorithm for reducing overfitting.

The deep learning model 221 uses highly flexible nonlinear algorithms.Accordingly, the result values of the deep learning model 221 mayexhibit a large deviation. The deviation between the result values ofthe deep learning models, i.e., output results, may be reduced byensembling the output results of the deep learning models 221 based onone or more of a majority vote-based ensemble, an unanimity-basedensemble, and an uncertainty-based ensemble.

In other words, in each of the deep learning modules 221, the internalparameters are trained differently depending on the training scheme,e.g., the sampling order and the randomness of the dropout. Even whentrained with the same data and the same deep learning models, each deeplearning model may exhibit different results. Therefore, use of one deeplearning model may lead to a risk of misjudgment. Therefore, the presentembodiment may generate various deep learning models and minimize therisk of misjudgment through an ensemble technique.

FIG. 12 is a block diagram of a dental medical record device accordingto another embodiment.

Referring to FIG. 12 , a dental medical record device 200 according toanother embodiment includes an input unit 210 receiving an image for apatient's oral cavity, a controller 220 reading the image for thepatient's oral cavity to determine the patient's oral cavity conditionand marking the patient's oral cavity condition on a prognosis chart,using a text format, symbols, or both text and symbols, and an outputunit 230 displaying the image and the prognosis chart.

The image may be one of a panoramic photo, a scan image, or a cameraimage obtained by one or two or more of radiography, oral camera,scanning device, and occlusion check data for the patient's oral cavity.

The patient's oral cavity condition may be at least one of the dentalformula, dental conditions, or surrounding structures of the teeth.

The controller 220 may continuously train artificial intelligence withcontinuously collected training images and accumulated chart data,allowing the artificial intelligence to read the image.

Artificial intelligence may automatically learn the features for inputvalues by being trained with a large amount of data from a deep neuralnetwork composed of a multi-layered network, and train the multi-layerednetwork to minimize errors in prediction accuracy and read images.

If a specific treatment site of the image is selected, the display unit230 displays information about the corresponding treatment site, and ifthe image of the corresponding treatment site is selected, displays atreatment summary memo window, and if the treatment summary memo windowis selected, outputs the chart for the corresponding treatment site.

For example, the controller 220 reads the image shown in FIG. 6 throughartificial intelligence and then determines the patient's periodontalcondition of alveolar bone loss.

After reading the image shown in FIG. 7A through artificialintelligence, the controller 220 determines the patient's periodontalcondition of dental caries.

After reading the image shown in FIG. 7B through artificialintelligence, the controller 220 marks crown in position A, inlay oramalgam in position B, post in position C, and root canal in position Din the step S110 of marking the prognosis chart.

As the information about the treatment site, the display unit 230displays “Jan. 2, 2020 #44, 45 extractions, 44 immediate implants,receipt: 200,000 won” as shown in FIG. 8A.

As shown in FIG. 8B, if the image of the corresponding treatment site isclicked, the display unit 230 may display a treatment summary memo(e.g., treatment title, date, and receipt) window, and if the treatmentsummary memo window is clicked, output a chart for the treatment site.

The controller 220 may enhance the accuracy of creating the prognosischart by continuously learning individually input information oraccumulated chart records through artificial intelligence using amachine learning model or a deep learning model.

As described above with reference to FIG. 11 , the deep learning model221 that may be used in the controller 220 may be a model in which theartificial neural networks is composed of multiple layers stacked.

As described above with reference to FIG. 11 , the deep learning model221 includes a feature extraction part 222 and an output part 224. Thedeep learning model, e.g., a CNN-based deep learning model, targetsoptimally training the parameters present in each individual layer inthe feature extraction part 222 and the output part 224. The deeplearning model 221 may apply random sampling (random in data order) anda regulation technique. The deep learning model 221 uses highly flexiblenonlinear algorithms.

The above-described dental medical record method 100 may be implementedby a computing device including at least some of a processor, a memory,a user input device, and a presentation device. The memory is a mediumthat stores computer-readable software, applications, program modules,routines, instructions, and/or data, coded to perform specific taskswhen executed by a processor. The processor may read and execute thecomputer-readable software, applications, program modules, routines,instructions, and/or data stored in the memory. The user input devicemay be a means for allowing the user to input a command to the processorto execute a specific task or to input data required for the executionof the specific task. The user input device may include a physical orvirtual keyboard or keypad, key button, mouse, joystick, trackball,touch-sensitive input means, or a microphone. The presentation devicemay include, e.g., a display, a printer, a speaker, or a vibrator.

The computing device may include various devices, such as smartphones,tablets, laptops, desktops, servers, clients, and the like. Thecomputing device may be a single stand-alone device and may include aplurality of computing devices operating in a distributed environmentcomposed of a plurality of computing devices cooperating with each otherthrough a communication network.

Further, the above-described dental medical record method 100 may beexecuted by a computing device that includes a processor and a memorystoring computer readable software, applications, program modules,routines, instructions, and/or data structures, coded to perform animage diagnosis method utilizing a deep learning model when executed bythe processor.

The present embodiments described above may be implemented throughvarious means. For example, the present embodiments may be implementedby various means, e.g., hardware, firmware, software, or a combinationthereof.

When implemented in hardware, the dental medical record method 100 usinga deep learning model according to the present embodiments may beimplemented by, e.g., one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, or micro-processors.

For example, the dental medical record method 100 according toembodiments may be implemented by an artificial intelligencesemiconductor device in which neurons and synapses of the deep neuralnetwork are implemented with semiconductor devices. In this case, thesemiconductor devices may be currently available semiconductor devices,e.g., SRAM, DRAM, or NAND or may be next-generation semiconductordevices, such as RRAM, STT MRAM, or PRAM, or may be combinationsthereof.

When the dental medical record method 100 according to embodiments isimplemented using an artificial intelligence semiconductor device, theresults (weights) of training the deep learning model with software maybe transferred to synaptic mimic devices disposed in an array, orlearning may be performed in the artificial intelligence semiconductordevice.

When implemented in firmware or hardware, the dental medical recordmethod 100 according to the present embodiments may be implemented inthe form of a device, procedure, or function performing theabove-described functions or operations. The software code may be storedin a memory unit and driven by a processor. The memory unit may bepositioned inside or outside the processor to exchange data with theprocessor by various known means.

The above-described terms, such as “system,” “processor,” “controller,”“component,” “module,” “interface,” “model,” or “unit,” described abovemay generally refer to computer-related entity hardware, a combinationof hardware and software, software, or software being executed. Forexample, the above-described components may be, but are not limited to,processes driven by a processor, processors, controllers, controlprocessors, entities, execution threads, programs, and/or computers. Forexample, both an application being executed by a controller or aprocessor and the controller or the processor may be the components. Oneor more components may reside within a process and/or thread ofexecution, and the components may be positioned in one device (e.g., asystem, a computing device, etc.) or distributed in two or more devices.

Meanwhile, another embodiment provides a computer program stored in acomputer recording medium for performing the above-described dentalmedical record method 100. Further, another embodiment provides acomputer-readable recording medium storing a program for realizing theabove-described dental medical record method.

The program recorded on the recording medium may be read, installed, andexecuted by a computer to execute the above-described steps.

As such, for the computer to read the program recorded on the recordingmedium and execute the implemented functions with the program, theabove-described program may include code coded in a computer language,such as C, C++, JAVA, or machine language, which the processor (CPU) ofthe computer may read through a computer device interface.

Such code may include a function code related to a function defining theabove-described functions or may include an execution procedure-relatedcontrol code necessary for the processor of the computer to execute theabove-described functions according to a predetermined procedure.

Further, the code may further include additional information necessaryfor the processor of the computer to execute the above-describedfunctions or memory reference-related code as to the position (oraddress) in the internal or external memory of the computer the mediashould reference.

Further, when the processor of the computer needs to communicate with,e.g., another computer or a server at a remote site to execute theabove-described functions, the code may further includecommunication-related code as to how the processor of the computershould communicate with the remote computer or server using thecommunication module of the computer and what information or mediashould be transmitted/received upon communication.

The above-described computer-readable recording medium may include,e.g., ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, or optical datastorage devices, or may also include carrier wave-type implementations(e.g., transmissions through the Internet).

Further, the computer-readable recording medium may be distributed tocomputer systems connected via a network, and computer-readable codesmay be stored and executed in a distributed manner.

The functional programs for implementing the present invention and codeand code segments related thereto may easily be inferred or changed byprogrammers of the technical field to which the present inventionpertains, considering, e.g., the system environments of the computerreading and executing the program.

The dental medical record method 100 may be implemented in the form ofrecording media including computer-executable instructions, such asapplication or program modules. The computer-readable medium may be anavailable medium that is accessible by a computer. The computer-readablestorage medium may include a volatile medium, a non-volatile medium, aseparable medium, and/or an inseparable medium. The computer-readablemedium may include a computer storage medium. The computer storagemedium may include a volatile medium, a non-volatile medium, a separablemedium, and/or an inseparable medium that is implemented in any methodor scheme to store computer-readable commands, data architecture,program modules, or other data or information.

In a non-transitory computer-readable storage medium storinginstructions that, when executed by one or more processors, enable theone or more processors to perform a dental medical record method, theinstructions are executed by the one or more processors to enable thecomputer device to read an image for a patient's oral cavity to mark thepatient's oral cavity condition on a prognosis chart, using a textformat, symbols, or both text and symbols and display the image and theprognosis chart.

The above-described dental medical record method 100 may be executed byan application installed on a terminal, including a platform equipped inthe terminal or a program included in the operating system of theterminal), or may be executed by an application (or program) installedby the user on a master terminal via an application providing server,such as a web server, associated with the service or method, anapplication, or an application store server. In such a sense, theabove-described glaucoma surgery result diagnosis method may beimplemented in an application or program installed as default on theterminal or installed directly by the user and may be recorded in arecording medium or storage medium readable by a terminal or computer.Although embodiments of the present invention have been described withreference to the accompanying drawings, It will be appreciated by one ofordinary skill in the art that the present disclosure may be implementedin other various specific forms without changing the essence ortechnical spirit of the present disclosure. Thus, it should be notedthat the above-described embodiments are provided as examples and shouldnot be interpreted as limiting. Each of the components may be separatedinto two or more units or modules to perform its function(s) oroperation(s), and two or more of the components may be integrated into asingle unit or module to perform their functions or operations.

It should be noted that the scope of the present invention is defined bythe appended claims rather than the described description of theembodiments and include all modifications or changes made to the claimsor equivalents of the claims.

CROSS-REFERENCE TO RELATED APPLICATION

The instant patent application claims priority under 35 U.S.C. 119(a) toKorean Patent Application Nos. 10-2020-0004128 and 10-2020-0182812,filed on Jan. 13, 2020 and Dec. 24, 2020, respectively, in the KoreanIntellectual Property Office, the disclosures of which are hereinincorporated by reference in their entireties. The present patentapplication claims priority to other applications to be filed in othercountries, the disclosures of which are also incorporated by referenceherein in their entireties.

1. A dental medical record method comprising: reading an image for a patient's oral cavity to mark the patient's oral cavity condition on a prognosis chart, using a text format, symbols, or both text and symbols; and displaying the image and the prognosis chart.
 2. The dental medical record method of claim 1, wherein the image is one of a panoramic photo, a scan image, or a camera image obtained by one or two or more of radiography, oral camera, scanning device, and occlusion check data for the patient's oral cavity, and wherein the patient's oral cavity condition is at least one of a dental formula, a dental condition, or a surrounding structure of teeth.
 3. The dental medical record method of claim 1, wherein in marking the prognosis chart, artificial intelligence is continuously trained with continuously collected training images and accumulated prognosis chart data to read the image.
 4. The dental medical record method of claim 1, wherein artificial intelligence automatically learns features for input values by being trained with a large amount of data from a deep neural network composed of a multi-layered network and train the multi-layered network to minimize errors in prediction accuracy and read the image.
 5. The dental medical record method of claim 4, wherein the artificial intelligence reads the image through a deep learning model including a feature extraction part that extracts features for the image by performing convolution and subsampling on the input image and an output part that marks the prognosis chart with the patient's oral cavity condition in the format of text, symbols, or both text and symbols, using the extracted features.
 6. The dental medical record method of claim 1, wherein in marking the prognosis chart, if a specific treatment site of the image is selected, information about the corresponding treatment site is displayed, and if the image of the corresponding treatment site is selected, a treatment summary memo window is displayed, and if the treatment summary memo window is selected, the chart for the corresponding treatment site is output.
 7. A dental medical record device, comprising: an input unit receiving an image for a patient's oral cavity; a controller reading the image for the patient's oral cavity to determine the patient's oral cavity condition and marking the patient's oral cavity condition on a prognosis chart, using a text format, symbols, or both text and symbols; and an output unit displaying the image and the prognosis chart.
 8. The dental medical record device of claim 7, wherein the image is one of a panoramic photo, a scan image, or a camera image obtained by one or two or more of radiography, oral camera, scanning device, and occlusion check data for the patient's oral cavity, and wherein the patient's oral cavity condition is at least one of a dental formula, a dental condition, or a surrounding structure of teeth.
 9. The dental medical record device of claim 7, wherein the controller continuously trains artificial intelligence with continuously collected training images and accumulated prognosis chart data to read the image.
 10. The dental medical record device of claim 7, wherein artificial intelligence automatically learns features for input values by being trained with a large amount of data from a deep neural network composed of a multi-layered network and train the multi-layered network to minimize errors in prediction accuracy and read the image.
 11. The dental medical record device of claim 10, wherein the artificial intelligence reads the image through a deep learning model including a feature extraction part that extracts features for the image by performing convolution and subsampling on the input image and an output part that marks the prognosis chart with the patient's oral cavity condition in the format of text, symbols, or both text and symbols, using the extracted features.
 12. The dental medical record device of claim 9, wherein if a specific treatment site of the image is selected, the display unit displays information about the corresponding treatment site, and if the image of the corresponding treatment site is selected, displays a treatment summary memo window, and if the treatment summary memo window is selected, outputs the chart for the corresponding treatment site.
 13. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, enable the one or more processors to perform a dental medical record method, the instructions comprising: reading an image for a patient's oral cavity to mark the patient's oral cavity condition on a prognosis chart, using a text format, symbols, or both text and symbols; and displaying the image and the prognosis chart. 